| Autor: |
Cristina Soviany, Sorin Soviany |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Principles of Data Science ISBN: 9783030439804 |
| DOI: |
10.1007/978-3-030-43981-1_11 |
| Popis: |
The actual major issues for application development in various domains, including the financial industry and the associated uses-cases, concern the ways in which big data can be approached in order to meet the real-case constraints. There are already available a lot of data analytics tools that can be successfully applied by the financial organizations in order to perform specific tasks in relationships with their partners and customers, respectively. A challenging use-case of data science in financial industry is represented by the fraud management in which the design solutions are based on supervised and unsupervised learning, in order to avoid the drawbacks of the legacy solutions based on rules. Innovative solutions also include anomaly detection in order to efficiently handle new cases that cannot be actually learned with the supervised predictive modeling-based approaches. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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